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Realistic Fill Backtesting Engine for Options
A SaaS backtesting and alert validation platform that defaults to realistic bid/ask fill assumptions instead of mid-price, allowing options traders and alert service operators to measure true strategy performance. Includes historical spread analysis by strike and moneyness, real-time spread monitoring, and auto-filtering of untradeable setups.
为什么这很重要
You run an options alert service or actively backtest strategies, and your numbers look fantastic on paper — 90% win rates, +20% average returns. But when you actually trade your own signals, buying at the ask and selling at the bid, the spread devours your edge and you are left at breakeven. The far-OTM plays that looked brilliant in backtesting are the worst culprits: market makers widen those spreads to absurd levels during moves, making them untradeable despite correct directional calls. You have started manually logging spreads and filtering to near-the-money strikes, but you have no automated way to validate whether your alerts are actually tradeable at realistic fills before publishing them to subscribers.
- · 专为 Semi-professional options traders and options alert service operators who need credible, fill-realistic performance metrics for their strategies and subscriber-facing reports 打造。
- · 最可能的变现方式：SaaS subscription with tiered pricing based on alert volume and number of strategies backtested。
痛点叙事
You run an options alert service or actively backtest strategies, and your numbers look fantastic on paper — 90% win rates, +20% average returns. But when you actually trade your own signals, buying at the ask and selling at the bid, the spread devours your edge and you are left at breakeven. The far-OTM plays that looked brilliant in backtesting are the worst culprits: market makers widen those spreads to absurd levels during moves, making them untradeable despite correct directional calls. You have started manually logging spreads and filtering to near-the-money strikes, but you have no automated way to validate whether your alerts are actually tradeable at realistic fills before publishing them to subscribers.
得分构成
市场信号
Go-to-Market 启动方案
Independent options alert service operators with 50-500 paying subscribers who need credible fill-realistic performance to retain and grow their subscriber base
Several thousand alert service operators and tens of thousands of serious retail options backtesters globally
Organic presence in options trading communities and targeted outreach to alert service operators
$49/month for individual backtesters, $149/month for alert service operators with public grading
15 paying users within 30 days of launch, with at least 3 alert service operators using the public grading badge
MVP 方案 · 1-2 周
- Set up Polygon.io or ORATS options data integration for historical bid/ask quotes
- Build core backtesting engine that evaluates strategies at buy-at-ask/sell-at-bid instead of mid-price
- Create spread analysis module that calculates spread-to-edge ratio for each historical trade
- Design simple web dashboard showing mid-price vs. realistic-fill performance comparison
- Build API endpoint to ingest strategy parameters and return fill-realistic backtest results
- Add historical spread database with filtering by underlying, strike, DTE, and moneyness
- Implement auto-filtering that flags and removes untradeable far-OTM setups from backtest results
- Create alert grading report that operators can share with subscribers showing realistic fill metrics
- Build comparison view showing strategy performance across strike zones (far-OTM vs. near-ATM vs. ITM)
- Add real-time spread monitor that checks current spread conditions before a signal is published
差异化
为什么这件事可能失败
自我反驳——最重要的信任度信号
- 1Premium options data feed costs ($500-2000/month) may make pricing prohibitive for individual traders, squeezing margins before reaching scale
- 2Established backtesting platforms like Thinkorswim, TradeStation, or OptionAlpha could add realistic-fill features relatively quickly, neutralizing the differentiation
- 3Options data licensing restrictions may prevent redistributing spread analytics to subscribers, limiting the alert-service-operator use case that has the highest willingness to pay
证据综述
AI 如何合成此洞察——无原话引用
Approximately 8 commenters described the core problem: strategies and alerts that look excellent at mid-price become breakeven or negative at realistic bid/ask fills. Multiple users confirmed they manually track spreads alongside signals and filter to near-the-money strikes to preserve tradeable win rates. Several noted that far-OTM contracts are the worst offenders, with market makers widening spreads dramatically during moves. The discussion revealed that no automated solution exists for validating alerts at realistic fills — users are doing this manually with spreadsheets and ad-hoc logging.
行动计划
在写代码之前，先验证这个商机
推荐下一步
直接做
需求信号强烈。痛点真实、付费意愿明确——启动 MVP 开发。
落地页文案包
基于真实 Reddit 评论整理的即用文案，可直接粘贴到落地页
主标题
Realistic Fill Backtesting Engine for Options
副标题
A SaaS backtesting and alert validation platform that defaults to realistic bid/ask fill assumptions instead of mid-price, allowing options traders and alert service operators to measure true strategy performance. Includes historical spread analysis by strike and moneyness, real-time spread monitoring, and auto-filtering of untradeable setups.
目标用户
适合：Semi-professional options traders and options alert service operators who need credible, fill-realistic performance metrics for their strategies and subscriber-facing reports
功能列表
✓ Backtesting engine using buy-at-ask/sell-at-bid fill assumptions by default ✓ Historical spread database searchable by underlying, strike, DTE, and market condition ✓ Spread-to-edge ratio scoring that flags strategies as tradeable or untradeable ✓ Alert grading dashboard that publishes realistic fill performance alongside mid-price metrics ✓ Real-time spread monitor that warns when current spreads make a signal untradeable
去哪里验证
把落地页链接发布到 r/r/options——这里就是这些痛点被发现的地方。
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